A comparison of Zayandehrood River water values for agriculture and the environment

River water management is challenging not only since they are open systems with changing physical structures, but also because the water values are mostly unknown over varied sectors. If policymakers grasp water values, water management will be more efficient. This research intends to examine the values of water in agriculture, which receives the most substantial portion of water resources, with the values of water in the environment in Isfahan located in the Zayandehrood River basin of Iran. The consequences of contingent valuation and production function methods revealed that per cubic metre value of water is 13 times higher in the environment than agriculture. The government should reconsider the higher value of the environment despite it is a non‐market value. The contingent valuation model additionally proved that women exhibited 21% more willingness to pay than men in order to protect the environment; however, they are paid less by 36%.     

Energy efficiency optimization of one-way and two-way DF relaying considering circuit power

In this paper, the energy efficiency (EE) of a decode and forward (DF) relay system is studied, where two sources communicate through a half-duplex relay node in one-way and two-way relaying strategies. Both the circuitry power and the transmission power of all nodes are taken into consideration. In addition, three different coding schemes for two-way DF relaying strategy with two phases and two-way DF relaying with three phases are considered. The aim is to maximize the EE of the system for a constant spectral efficiency (SE). For this purpose, the transmission time and the transmission power of each node are optimized. Simulations are used to compare the EE–SE curve of different DF strategies with one-way and two-way amplify and forward (AF) strategies and direct transmission (DT), to find the best energy efficient strategy in different SE conditions. Analytical and simulation results demonstrate that in low SE conditions, DF relaying strategies are more energy efficient compared to that of AF strategies and DT. However, in high SE conditions, the EE of two-way AF relaying and DT strategy outperform some of the DF relaying strategies. In simulations, the impact of different circuitry power and different channel conditions on the EE–SE curves are also investigated.     

An Enhanced Cooperative Communication Scheme for Physical Uplink Shared Channel in NB-IoT

Wireless Personal Communications - Smart Home is one of the most established applications of the Internet of Things. Almost every equipment we use in our daily life—appliances, electric...     

EEG‐based brain source localization using visual stimuli

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others.     

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers.     

Gold nanocages in cancer diagnosis,therapy, and theranostics: A brief review

Regarding the increasing number of cancer patients, the global burden of this disease is continuing to grow. Despite a considerable improvement in the diagnosis and treatment of various types of cancer, new diagnosis and treatment strategies are required. Nanotechnology, as an interesting and advanced field in medicine, is aimed to further advance both cancer diagnosis and treatment. Gold nanocages (AuNCs), with hollow interiors and porous walls, have received a great deal of interest in various biomedical applications such as diagnosis, imaging, drug delivery, and hyperthermia therapy due to their special physicochemical characteristics including the porous structure and surface functionalization as well as optical and photothermal properties. This review is focused on recent developments in therapeutic and diagnostic and applications of AuNCs with an emphasis on their theranostic applications in cancer diseases.     

Solution‐Processed GaSe Nanoflake‐Based Films for Photoelectrochemical Water Splitting and Photoelectrochemical‐Type Photodetectors

Gallium selenide (GaSe) is a layered compound, which has been exploited in nonlinear optical applications and photodetectors due to its anisotropic structure and pseudodirect optical gap. Theoretical studies predict that its 2D form is a potential photocatalyst for water splitting reactions. Herein, the photoelectrochemical (PEC) characterization of GaSe nanoflakes (single‐/few‐layer flakes), produced via liquid phase exfoliation, for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in both acidic and alkaline media is reported. In 0.5 m H₂SO₄, the GaSe photoelectrodes display the best PEC performance, corresponding to a ratiometric power‐saved metric for HER (Φ_saved,HER) of 0.09% and a ratiometric power‐saved metric for OER (Φ_saved,OER) of 0.25%. When used as PEC‐type photodetectors, GaSe photoelectrodes show a responsivity of ≈0.16 A W^?1 upon 455 nm illumination at a light intensity of 63.5 µW cm^?2 and applied potential of ?0.3 V versus reversible hydrogen electrode (RHE). Stability tests of GaSe photodetectors demonstrated a durable operation over tens of cathodic linear sweep voltammetry scans in 0.5 m H₂SO₄ for HER. In contrast, degradation of photoelectrodes occurred in both alkaline and anodic operation due to the highly oxidizing environment and O₂‐induced (photo)oxidation effects. The results provide new insight into the PEC properties of GaSe nanoflakes for their exploitation in photoelectrocatalysis, PEC‐type photodetectors, and (bio)sensors.